"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
       from huggingface_hub import snapshot_download
       from pyctcdecode import BeamSearchDecoderCTC

       from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
       from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
       from transformers import WavaVecaForCTC







@require_pyctcdecode
class 			UpperCamelCase						(     unittest.TestCase    ):
   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        lowercase_      :							str						=      '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
        lowercase_      :							Dict						=      dict(zip(_A	,range(len(_A     )     )     )     )

        lowercase_      :							List[Any]						=      {
            '''unk_token''': '''<unk>''',
            '''bos_token''': '''<s>''',
            '''eos_token''': '''</s>''',
        }
        lowercase_      :							List[str]						=      {
            '''feature_size''': 1,
            '''padding_value''': 0.0,
            '''sampling_rate''': 1_6000,
            '''return_attention_mask''': False,
            '''do_normalize''': True,
        }

        lowercase_      :							Optional[int]						=      tempfile.mkdtemp()
        lowercase_      :							List[str]						=      os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']     )
        lowercase_      :							Tuple						=      os.path.join(self.tmpdirname	,_A     )
        with open(self.vocab_file	,'w'	,encoding='utf-8'     ) as fp:
             fp.write(json.dumps(_A     ) + '\n'     )

        with open(self.feature_extraction_file	,'w'	,encoding='utf-8'     ) as fp:
             fp.write(json.dumps(_A     ) + '\n'     )

        # load decoder from hub
        lowercase_      :							Optional[Any]						=      '''hf-internal-testing/ngram-beam-search-decoder'''
   def      _UpperCAmelCase  (     self	,**__UpperCamelCase     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							Any						=      self.add_kwargs_tokens_map.copy()
        kwargs.update(_A     )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,**_A     )
   def      _UpperCAmelCase  (     self	,**__UpperCamelCase     )      -> int:


        '''simple docstring'''


        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,**_A     )
   def      _UpperCAmelCase  (     self	,**__UpperCamelCase     )      -> str:


        '''simple docstring'''


        return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name	,**_A     )
   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        shutil.rmtree(self.tmpdirname     )
   def      _UpperCAmelCase  (     self     )      -> Optional[Any]:


        '''simple docstring'''


        lowercase_      :							Optional[int]						=      self.get_tokenizer()
        lowercase_      :							List[str]						=      self.get_feature_extractor()
        lowercase_      :							List[Any]						=      self.get_decoder()

        lowercase_      :							Any						=      WavaVecaProcessorWithLM(tokenizer=_A	,feature_extractor=_A	,decoder=_A     )

        processor.save_pretrained(self.tmpdirname     )
        lowercase_      :							Any						=      WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname     )

        # tokenizer
        self.assertEqual(processor.tokenizer.get_vocab()	,tokenizer.get_vocab()     )
        self.assertIsInstance(processor.tokenizer	,_A     )

        # feature extractor
        self.assertEqual(processor.feature_extractor.to_json_string()	,feature_extractor.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor	,_A     )

        # decoder
        self.assertEqual(processor.decoder._alphabet.labels	,decoder._alphabet.labels     )
        self.assertEqual(
            processor.decoder.model_container[decoder._model_key]._unigram_set	,decoder.model_container[decoder._model_key]._unigram_set	,)
        self.assertIsInstance(processor.decoder	,_A     )
   def      _UpperCAmelCase  (     self     )      -> Any:


        '''simple docstring'''


        lowercase_      :							Union[str, Any]						=      WavaVecaProcessorWithLM(
            tokenizer=self.get_tokenizer()	,feature_extractor=self.get_feature_extractor()	,decoder=self.get_decoder()     )
        processor.save_pretrained(self.tmpdirname     )

        # make sure that error is thrown when decoder alphabet doesn't match
        lowercase_      :							Optional[Any]						=      WavaVecaProcessorWithLM.from_pretrained(
            self.tmpdirname	,alpha=5.0	,beta=3.0	,score_boundary=-7.0	,unk_score_offset=3     )

        # decoder
        self.assertEqual(processor.language_model.alpha	,5.0     )
        self.assertEqual(processor.language_model.beta	,3.0     )
        self.assertEqual(processor.language_model.score_boundary	,-7.0     )
        self.assertEqual(processor.language_model.unk_score_offset	,3     )
   def      _UpperCAmelCase  (     self     )      -> Optional[Any]:


        '''simple docstring'''


        lowercase_      :							str						=      self.get_tokenizer()
        # add token to trigger raise
        tokenizer.add_tokens(['xx']     )
        with self.assertRaisesRegex(_A	,'include'     ):
             WavaVecaProcessorWithLM(
                 tokenizer=_A	,feature_extractor=self.get_feature_extractor()	,decoder=self.get_decoder()     )
   def      _UpperCAmelCase  (     self     )      -> List[Any]:


        '''simple docstring'''


        lowercase_      :							str						=      self.get_feature_extractor()
        lowercase_      :							List[str]						=      self.get_tokenizer()
        lowercase_      :							Tuple						=      self.get_decoder()

        lowercase_      :							Union[str, Any]						=      WavaVecaProcessorWithLM(tokenizer=_A	,feature_extractor=_A	,decoder=_A     )

        lowercase_      :							Dict						=      floats_list((3, 1000)     )

        lowercase_      :							Union[str, Any]						=      feature_extractor(_A	,return_tensors='np'     )
        lowercase_      :							List[Any]						=      processor(_A	,return_tensors='np'     )

        for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()	,input_processor[key].sum()	,delta=1e-2     )
   def      _UpperCAmelCase  (     self     )      -> Dict:


        '''simple docstring'''


        lowercase_      :							Dict						=      self.get_feature_extractor()
        lowercase_      :							List[str]						=      self.get_tokenizer()
        lowercase_      :							List[str]						=      self.get_decoder()

        lowercase_      :							List[str]						=      WavaVecaProcessorWithLM(tokenizer=_A	,feature_extractor=_A	,decoder=_A     )

        lowercase_      :							str						=      '''This is a test string'''

        lowercase_      :							List[str]						=      processor(text=_A     )

        lowercase_      :							str						=      tokenizer(_A     )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]	,encoded_processor[key]     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase=(2, 10, 16)	,__UpperCamelCase=77     )      -> Union[str, Any]:


        '''simple docstring'''


        np.random.seed(_A     )
        return np.random.rand(*_A     )
   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							List[Any]						=      self.get_feature_extractor()
        lowercase_      :							str						=      self.get_tokenizer()
        lowercase_      :							Optional[int]						=      self.get_decoder()

        lowercase_      :							int						=      WavaVecaProcessorWithLM(tokenizer=_A	,feature_extractor=_A	,decoder=_A     )

        lowercase_      :							List[str]						=      self._get_dummy_logits(shape=(10, 16)	,seed=13     )

        lowercase_      :							Union[str, Any]						=      processor.decode(_A     )

        lowercase_      :							int						=      decoder.decode_beams(_A     )[0]

        self.assertEqual(decoded_decoder[0]	,decoded_processor.text     )
        self.assertEqual('</s> <s> </s>'	,decoded_processor.text     )
        self.assertEqual(decoded_decoder[-2]	,decoded_processor.logit_score     )
        self.assertEqual(decoded_decoder[-1]	,decoded_processor.lm_score     )
   @parameterized.expand([[None], ['fork'], ['spawn']]     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> Tuple:


        '''simple docstring'''


        lowercase_      :							Union[str, Any]						=      self.get_feature_extractor()
        lowercase_      :							List[str]						=      self.get_tokenizer()
        lowercase_      :							Any						=      self.get_decoder()

        lowercase_      :							str						=      WavaVecaProcessorWithLM(tokenizer=_A	,feature_extractor=_A	,decoder=_A     )

        lowercase_      :							str						=      self._get_dummy_logits()

        # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
        #       otherwise, the LM won't be available to the pool's sub-processes.
        # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
        if pool_context is None:
             lowercase_      :							int						=      processor.batch_decode(_A     )
        else:
             with get_context(_A     ).Pool() as pool:
                  lowercase_      :							str						=      processor.batch_decode(_A	,_A     )

        lowercase_      :							Tuple						=      list(_A     )

        with get_context('fork'     ).Pool() as p:
             lowercase_      :							Optional[int]						=      decoder.decode_beams_batch(_A	,_A     )

        lowercase_      :							Tuple						=      [], [], []
        for beams in decoded_beams:
             texts_decoder.append(beams[0][0]     )
             logit_scores_decoder.append(beams[0][-2]     )
             lm_scores_decoder.append(beams[0][-1]     )

        self.assertListEqual(_A	,decoded_processor.text     )
        self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']	,decoded_processor.text     )
        self.assertListEqual(_A	,decoded_processor.logit_score     )
        self.assertListEqual(_A	,decoded_processor.lm_score     )
   def      _UpperCAmelCase  (     self     )      -> str:


        '''simple docstring'''


        lowercase_      :							Optional[int]						=      self.get_feature_extractor()
        lowercase_      :							str						=      self.get_tokenizer()
        lowercase_      :							Tuple						=      self.get_decoder()

        lowercase_      :							Any						=      WavaVecaProcessorWithLM(tokenizer=_A	,feature_extractor=_A	,decoder=_A     )

        lowercase_      :							Dict						=      self._get_dummy_logits()

        lowercase_      :							Optional[int]						=      15
        lowercase_      :							Optional[Any]						=      -20.0
        lowercase_      :							List[Any]						=      -4.0

        lowercase_      :							Tuple						=      processor.batch_decode(
            _A	,beam_width=_A	,beam_prune_logp=_A	,token_min_logp=_A	,)
        lowercase_      :							Optional[int]						=      decoded_processor_out.text

        lowercase_      :							int						=      list(_A     )

        with get_context('fork'     ).Pool() as pool:
             lowercase_      :							Dict						=      decoder.decode_beams_batch(
                 _A	,_A	,beam_width=_A	,beam_prune_logp=_A	,token_min_logp=_A	,)

        lowercase_      :							Optional[int]						=      [d[0][0] for d in decoded_decoder_out]
        lowercase_      :							str						=      [d[0][2] for d in decoded_decoder_out]
        lowercase_      :							int						=      [d[0][3] for d in decoded_decoder_out]

        self.assertListEqual(_A	,_A     )
        self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']	,_A     )

        self.assertTrue(np.array_equal(_A	,decoded_processor_out.logit_score     )     )
        self.assertTrue(np.allclose([-20.054, -18.447]	,_A	,atol=1e-3     )     )

        self.assertTrue(np.array_equal(_A	,decoded_processor_out.lm_score     )     )
        self.assertTrue(np.allclose([-15.554, -13.9474]	,_A	,atol=1e-3     )     )
   def      _UpperCAmelCase  (     self     )      -> Tuple:


        '''simple docstring'''


        lowercase_      :							Optional[Any]						=      self.get_feature_extractor()
        lowercase_      :							Union[str, Any]						=      self.get_tokenizer()
        lowercase_      :							Optional[Any]						=      self.get_decoder()

        lowercase_      :							List[Any]						=      WavaVecaProcessorWithLM(tokenizer=_A	,feature_extractor=_A	,decoder=_A     )

        lowercase_      :							Optional[int]						=      self._get_dummy_logits()

        lowercase_      :							List[Any]						=      2.0
        lowercase_      :							Optional[Any]						=      5.0
        lowercase_      :							List[str]						=      -20.0
        lowercase_      :							Union[str, Any]						=      True

        lowercase_      :							Optional[Any]						=      processor.batch_decode(
            _A	,alpha=_A	,beta=_A	,unk_score_offset=_A	,lm_score_boundary=_A	,)
        lowercase_      :							List[Any]						=      decoded_processor_out.text

        lowercase_      :							Dict						=      list(_A     )
        decoder.reset_params(
            alpha=_A	,beta=_A	,unk_score_offset=_A	,lm_score_boundary=_A	,)

        with get_context('fork'     ).Pool() as pool:
             lowercase_      :							str						=      decoder.decode_beams_batch(
                 _A	,_A	,)

        lowercase_      :							Optional[Any]						=      [d[0][0] for d in decoded_decoder_out]

        self.assertListEqual(_A	,_A     )
        self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']	,_A     )
        lowercase_      :							Any						=      processor.decoder.model_container[processor.decoder._model_key]
        self.assertEqual(lm_model.alpha	,2.0     )
        self.assertEqual(lm_model.beta	,5.0     )
        self.assertEqual(lm_model.unk_score_offset	,-20.0     )
        self.assertEqual(lm_model.score_boundary	,_A     )
   def      _UpperCAmelCase  (     self     )      -> Union[str, Any]:


        '''simple docstring'''


        lowercase_      :							Optional[int]						=      WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'     )

        lowercase_      :							int						=      processor.decoder.model_container[processor.decoder._model_key]
        lowercase_      :							Any						=      Path(language_model._kenlm_model.path.decode('utf-8'     )     ).parent.parent.absolute()

        lowercase_      :							List[str]						=      os.listdir(_A     )
        lowercase_      :							Optional[int]						=      ['''alphabet.json''', '''language_model''']

        downloaded_decoder_files.sort()
        expected_decoder_files.sort()

        # test that only decoder relevant files from
        # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
        # are downloaded and none of the rest (e.g. README.md, ...)
        self.assertListEqual(_A	,_A     )
   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							Dict						=      snapshot_download('hf-internal-testing/processor_with_lm'     )

        lowercase_      :							Union[str, Any]						=      WavaVecaProcessorWithLM.from_pretrained(_A     )

        lowercase_      :							Optional[int]						=      processor.decoder.model_container[processor.decoder._model_key]
        lowercase_      :							Tuple						=      Path(language_model._kenlm_model.path.decode('utf-8'     )     ).parent.parent.absolute()

        lowercase_      :							Optional[int]						=      os.listdir(_A     )
        lowercase_      :							Dict						=      os.listdir(_A     )

        local_decoder_files.sort()
        expected_decoder_files.sort()

        # test that both decoder form hub and local files in cache are the same
        self.assertListEqual(_A	,_A     )
   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							Optional[Any]						=      WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'     )
        lowercase_      :							Union[str, Any]						=      AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'     )

        lowercase_      :							int						=      floats_list((3, 1000)     )

        lowercase_      :							str						=      processor_wavaveca(_A	,return_tensors='np'     )
        lowercase_      :							int						=      processor_auto(_A	,return_tensors='np'     )

        for key in input_wavaveca.keys():
             self.assertAlmostEqual(input_wavaveca[key].sum()	,input_auto[key].sum()	,delta=1e-2     )

        lowercase_      :							int						=      self._get_dummy_logits()

        lowercase_      :							Any						=      processor_wavaveca.batch_decode(_A     )
        lowercase_      :							int						=      processor_auto.batch_decode(_A     )

        self.assertListEqual(decoded_wavaveca.text	,decoded_auto.text     )
   def      _UpperCAmelCase  (     self     )      -> Union[str, Any]:


        '''simple docstring'''


        lowercase_      :							Any						=      self.get_feature_extractor()
        lowercase_      :							Optional[int]						=      self.get_tokenizer()
        lowercase_      :							Any						=      self.get_decoder()

        lowercase_      :							Optional[int]						=      WavaVecaProcessorWithLM(tokenizer=_A	,feature_extractor=_A	,decoder=_A     )

        self.assertListEqual(
            processor.model_input_names	,feature_extractor.model_input_names	,msg='`processor` and `feature_extractor` model input names do not match'	,)
   @staticmethod
   def      _UpperCAmelCase  (     __UpperCamelCase	,__UpperCamelCase     )      -> Optional[Any]:


        '''simple docstring'''


        lowercase_      :							Tuple						=      [d[key] for d in offsets]
        return retrieved_list
   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        lowercase_      :							Any						=      WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'     )
        lowercase_      :							Any						=      self._get_dummy_logits()[0]

        lowercase_      :							List[str]						=      processor.decode(_A	,output_word_offsets=_A     )
        # check Wav2Vec2CTCTokenizerOutput keys for word
        self.assertEqual(len(outputs.keys()     )	,4     )
        self.assertTrue('text' in outputs     )
        self.assertTrue('word_offsets' in outputs     )
        self.assertTrue(isinstance(_A	,_A     )     )

        self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets']	,'word'     )     )	,outputs.text     )
        self.assertListEqual(self.get_from_offsets(outputs['word_offsets']	,'word'     )	,['<s>', '<s>', '</s>']     )
        self.assertListEqual(self.get_from_offsets(outputs['word_offsets']	,'start_offset'     )	,[0, 2, 4]     )
        self.assertListEqual(self.get_from_offsets(outputs['word_offsets']	,'end_offset'     )	,[1, 3, 5]     )
   def      _UpperCAmelCase  (     self     )      -> List[Any]:


        '''simple docstring'''


        lowercase_      :							Any						=      WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'     )
        lowercase_      :							List[str]						=      self._get_dummy_logits()

        lowercase_      :							Optional[Any]						=      processor.batch_decode(_A	,output_word_offsets=_A     )

        # check Wav2Vec2CTCTokenizerOutput keys for word
        self.assertEqual(len(outputs.keys()     )	,4     )
        self.assertTrue('text' in outputs     )
        self.assertTrue('word_offsets' in outputs     )
        self.assertTrue(isinstance(_A	,_A     )     )

        self.assertListEqual(
            [' '.join(self.get_from_offsets(_A	,'word'     )     ) for o in outputs['word_offsets']]	,outputs.text     )
        self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]	,'word'     )	,['<s>', '<s>', '</s>']     )
        self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]	,'start_offset'     )	,[0, 2, 4]     )
        self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]	,'end_offset'     )	,[1, 3, 5]     )







   @slow
   @require_torch
   @require_torchaudio
   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        import torch

        lowercase_      :							Optional[int]						=      load_dataset('common_voice'	,'en'	,split='train'	,streaming=_A     )
        lowercase_      :							List[str]						=      ds.cast_column('audio'	,datasets.Audio(sampling_rate=1_6000     )     )
        lowercase_      :							Optional[Any]						=      iter(_A     )
        lowercase_      :							str						=      next(_A     )

        lowercase_      :							Tuple						=      AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'     )
        lowercase_      :							Optional[Any]						=      WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'     )

        # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
        lowercase_      :							Union[str, Any]						=      processor(sample['audio']['array']	,return_tensors='pt'     ).input_values

        with torch.no_grad():
             lowercase_      :							Union[str, Any]						=      model(_A     ).logits.cpu().numpy()

        lowercase_      :							int						=      processor.decode(logits[0]	,output_word_offsets=_A     )

        lowercase_      :							Optional[Any]						=      model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
        lowercase_      :							Tuple						=      [
            {
                '''start_time''': d['''start_offset'''] * time_offset,
                '''end_time''': d['''end_offset'''] * time_offset,
                '''word''': d['''word'''],
            }
            for d in output['''word_offsets''']
        ]

        lowercase_      :							Union[str, Any]						=      '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

        # output words
        self.assertEqual(' '.join(self.get_from_offsets(_A	,'word'     )     )	,_A     )
        self.assertEqual(' '.join(self.get_from_offsets(_A	,'word'     )     )	,output.text     )

        # output times
        lowercase_      :							List[str]						=      torch.tensor(self.get_from_offsets(_A	,'start_time'     )     )
        lowercase_      :							Any						=      torch.tensor(self.get_from_offsets(_A	,'end_time'     )     )

        # fmt: off
        lowercase_      :							List[str]						=      torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]     )
        lowercase_      :							Any						=      torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]     )
        # fmt: on

        self.assertTrue(torch.allclose(_A	,_A	,atol=0.01     )     )
        self.assertTrue(torch.allclose(_A	,_A	,atol=0.01     )     )









'''simple docstring'''





from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCamelCase						:						Any  =					tuple[int, int]


class  snake_case__      :




					def __init__(  self  :		List[str]				, _A  :		set[int]				, _A  :		Mapping[EdgeT, int]      )    ->							None:
								UpperCAmelCase_      :       set[int]          =       vertices
								UpperCAmelCase_      :       dict[EdgeT, int]          =       {
								    (min(_A      ), max(_A      )): weight for edge, weight in edges.items()
								}




					def 		A      (  self  :		Union[str, Any]				, _A  :		EdgeT				, _A  :		int      )    ->							None:
								self.vertices.add(edge[0]      )
								self.vertices.add(edge[1]      )
								UpperCAmelCase_      :       List[str]          =       weight





					def 		A      (  self  :		str      )    ->							Graph:
								UpperCAmelCase_      :       Graph          =       Graph({min(self.vertices      )}				, {}      )
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int

								while len(subgraph.vertices      ) < len(self.vertices      ):
											UpperCAmelCase_      :       Optional[Any]          =       max(self.edges.values()      ) + 1
											for edge, weight in self.edges.items():
														if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																	if weight < min_weight:
																				UpperCAmelCase_      :       Dict          =       edge
																				UpperCAmelCase_      :       Tuple          =       weight

											subgraph.add_edge(_A				, _A      )

								return subgraph






def __UpperCAmelCase   (					A					:			str = "p107_network.txt"		)    ->   int:
			UpperCAmelCase_      :       str          =       os.path.abspath(os.path.dirname(A		)		)
			UpperCAmelCase_      :       str          =       os.path.join(A			,       A		)
			UpperCAmelCase_      :       dict[EdgeT, int]          =       {}
			UpperCAmelCase_      :       list[str]
			UpperCAmelCase_      :       int
			UpperCAmelCase_      :       int

			with open(A		) as f:
						UpperCAmelCase_      :       int          =       f.read().strip().split('''\n'''		)

			UpperCAmelCase_      :       Any          =       [line.split(''','''		) for line in data]

			for edgea in range(1			,       len(A		)		):
						for edgea in range(A		):
									if adjaceny_matrix[edgea][edgea] != "-":
												UpperCAmelCase_      :       Optional[int]          =       int(adjaceny_matrix[edgea][edgea]		)

			UpperCAmelCase_      :       Graph          =       Graph(set(range(len(A		)		)		)			,       A		)

			UpperCAmelCase_      :       Graph          =       graph.prims_algorithm()

			UpperCAmelCase_      :       int          =       sum(graph.edges.values()		)
			UpperCAmelCase_      :       int          =       sum(subgraph.edges.values()		)

			return initial_total - optimal_total


if __name__ == "__main__":
						print(f'''{solution() = }''')


